Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds valuable behavioral details beyond that: source data (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs), handling of off-calendar fiscal years, sorting by primary metric, and inclusion of citation URIs. There is no contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded with trigger phrases and a clear directive. Every sentence contributes unique information—usage triggers, data sources, sorting behavior, and efficiency gains—without redundancy or filler. It is structured to be scannable and immediately actionable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the 2-parameter schema, strong annotations, and no output schema, the description fully covers what the tool returns (paired data, citation URIs), how results are ordered, and what each type does. It also names the alternative approach it replaces, making its role in the toolset clear. No significant gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although schema coverage is 100%, the description enriches the parameters substantially: it explains what 'type' maps to in terms of data sources, gives concrete examples for 'values' (tickers vs drug names), and clarifies output behavior (sorted by primary metric). This goes well beyond the schema's terse property descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('compare', 'rank', 'side-by-side') with a clear resource ('companies or drugs'), and explicitly differentiates itself from sequential single-entity lookups. It also enumerates concrete query phrasings that trigger the tool's use, making its purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage criteria: trigger phrases like 'X vs Y', 'which is bigger', 'rank these companies', and a strong directive to always prefer this over sequential single-pack lookups when comparing entities. It also gives per-type data sources, clarifying when company vs drug comparison is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.